> ## Documentation Index > Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt > Use this file to discover all available pages before exploring further. # What is Lab? > An overview of Lab, Prime Intellect's open research platform for post-training Lab is Prime Intellect's open research platform for post-training. It unifies the [Environments Hub](/tutorials-environments/environments), Hosted Training, and Hosted Evaluations into a full-stack platform for reinforcement learning research and optimization. The platform enables the entire lifecycle of post-training research — from large-scale agentic RL, to inference and evaluation — without needing to worry about the costs of massive GPU clusters or the details of low-level algorithm implementation. ## Core Concepts ### Environments Lab is built around **environments**, which contain everything needed to run a model on a task: * A **dataset** of tasks (input prompts with optional ground-truth answers) * A **harness** for the model (tools, sandboxes, context management, multi-turn interactions) * A **rubric** to score the model's performance (reward functions, metrics) Environments are self-contained Python modules built with the [verifiers](https://github.com/PrimeIntellect-ai/verifiers) library. They can be used to train models with reinforcement learning, evaluate capabilities, generate synthetic data, optimize prompts, experiment with agent harnesses, and more. See [The Environment Model](/hosted-training/environment-model) for a detailed breakdown of environment types and the rollout loop. ### Environments Hub The [Environments Hub](https://app.primeintellect.ai/dashboard/environments) is a community registry where you can browse and deploy open-source environments, or create and share your own. Environments support running evaluations with any OpenAI-compatible model endpoint, or training any supported open-weights model via Hosted Training. The hub includes a range of pre-built environments spanning math, coding, games, search and tool use, multimodal tasks, and more — as well as validated implementations of popular benchmarks like AIME, MATH-500, and Humanity's Last Exam. ### Hosted Training Hosted Training allows you to run large-scale RL training experiments in your own environments without managing infrastructure. Prime Intellect currently supports agentic RL with LoRA, built on the open-source [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) training library. ### Hosted Evaluations Run evaluations against any environment directly from the web UI or CLI, using Prime Inference or your own OpenAI-compatible endpoints. Results are saved and viewable in the terminal UI or on the dashboard. ## Architecture The platform is powered by the [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) training framework. The three key components of the architecture are: * **Trainer** — Consumes batches and updates model weights using LoRA * **Inference** — Serves the model via an OpenAI-compatible API with live weight updating * **Orchestrator** — Manages environment logic, schedules rollout requests, and coordinates the training loop For Hosted Training, each run is allocated a dedicated Orchestrator which manages environment logic, while Prime Intellect manages multi-tenant LoRA deployments for the Trainer and Inference components. This architecture allows shared hardware across runs, enabling high efficiency and per-token pricing. ## What You Can Do with Lab | Capability | Description | | ----------------------------- | --------------------------------------------------------------------------------- | | **RL Training** | Train open-weights models with reinforcement learning on your custom environments | | **Evaluation** | Benchmark any OpenAI-compatible model against environments on the Hub | | **Environment Development** | Build, test, and share environments using the verifiers library | | **Synthetic Data Generation** | Generate rollout data for downstream use | ## Key Libraries Lab integrates with two core open-source libraries: **[verifiers](https://github.com/PrimeIntellect-ai/verifiers)** — The environment framework. Provides building blocks for datasets, rubrics, tools, sandboxes, and multi-turn interactions. Environments built with verifiers are portable across local evaluation, hosted evaluation, and Hosted Training. **[prime-rl](https://github.com/PrimeIntellect-ai/prime-rl)** — The training framework. Supports fully asynchronous distributed RL at scale with FSDP2 training and vLLM inference. Used both for self-hosted training on your own GPUs and as the backend for Hosted Training. ## Getting Access To get started with Lab: 1. [Create a Prime Intellect account](https://app.primeintellect.ai) 2. Install the Prime CLI: `uv tool install prime` 3. Authenticate: `prime login` 4. Set up a workspace: `prime lab setup` Set up your workspace and run your first environment. Walk through a complete Hosted Training run.